2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDHLT-18.2
Paper Title A Large-Scale Chinese Long-text Extractive Summarization Corpus
Authors Kai Chen, Guanyu Fu, Qingcai Chen, Baotian Hu, Harbin Institute of Technology, Shenzhen, China
SessionHLT-18: Language Understanding 6: Summarization and Comprehension
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-LRES] Language Resources and Systems
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, large-scale datasets have vastly facilitated the development in nearly domains of Natural Language Processing. However, lacking large scale Chinese corpus is still a critical bottleneck for further research on deep text summarization methods. In this paper, we publish a large-scale Chinese Long-text Extractive Summarization corpus named CLES. The CLES contains about 104K pairs, which is originally collected from Sina Weibo. To verify the quality of the corpus, we also manually tagged the relevance score of 5,000 pairs. Our benchmark models on the proposed corpus include conventional deep learning based extractive models and several pre-trained Bert-based algorithms. Their performances are reported and briefly analyzed to facilitate further research on the corpus. We will release this corpus for further research.